Glitch Classification and Clustering for LIGO with Deep Transfer Learning
By Daniel George, Hongyu Shen, E. A. Huerta. 2017
94 citations recorded by the source corpus.
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian noise transients known as glitches, since their high occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational wave signals. Therefore, successfully identifying and excising glitches is of utmost importance to detect and characterize gravitational waves. In this article, we present the first application of Deep Learning combined with Transfer Learning for glitch classification, using real data from LIGO's first discovery campaign labeled by Gravity Spy, showing that knowledge from pre-trained models for real-world object recognition can be transferred for classifying spectrograms of glitches. We demonstrate that this method enables the optimal use of very deep convolutional neural networks for glitch classification given small unbalanced training datasets, significantly reduces the training time, and achieves state-of-the-art accuracy above 98.8%. Once trained via transfer learning, we show that the networks can be truncated and used as feature extractors for unsupervised clustering to automatically group together new classes of glitches and anomalies. This novel capability is of critical importance to identify and remove new types of glitches which will occur as the LIGO/Virgo detectors gradually attain design sensitivity.
Subfields: Astronomy and Astrophysics, Oceanography
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Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “learning has been used for classification [9], anomaly detection [10] and”
- A Survey on Deep Transfer Learning (2018, background): “[6] apply deep transfer learning to transfer knowledge from real-world object recognition tasks to glitch classifier for the detector of multiple gravitational wave signals.”
- The Zwicky Transient Facility: Science Objectives (2019, unknown): “Recent advances in the field of transfer learning may make it possible to use training data sets generated with other surveys and use the information in them to generate accurate classifications for ZTF sources (Aswolinskiy & Hammer 2017; George et al. 2017).”
- Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data (2017, background): “This suggests that Deep Filtering could also serve as an alternative to existing glitch classification algorithms [30, 31, 32, 33, 34, 27, 35].”